ALBERT

Description: Subsurface nutrients on the Scotian Shelf, an ocean region at the convergence of the subpolar and subtropical western boundary currents (i.e., Labrador Current and Gulf Stream), are chiefly modulated by upstream shelf and slope waters. Yet little is known about long-term fluctuations in the advective transport of nutrients to the shelf. To examine the relationships between subsurface nutrient concentrations and dominant slope water masses at the Scotian Shelf break, we assembled all available hydrographic data (temperature, salinity) and dissolved nutrient data (nitrate, phosphate, silicate) for the period 1975-2020. Hydrographic and nutrient data were extracted from the Fisheries and Oceans Canada (DFO) data archives MEDS (Marine Environmental Data Section Archive; DFO, 2023a) and BioChem (DFO, 2023b; Devine et al., 2014), respectively, and predominantly include data from current DFO programs (e.g., Atlantic Zone Monitoring Program (AZMP)) and legacy datasets. Hydrographic data consist of vertical water column profiles collected using a conductivity-temperature-depth (CTD) profiler, generally mounted to a rosette sampler equipped with Niskin bottles for discrete water and nutrient sampling (Mitchel et al., 2002). Nutrient (nitrate, phosphate, silicate) measurements generally followed well established colorimetric techniques outlined in detail in the AZMP sampling protocol (Mitchell et al., 2002). Only nutrient data that passed initial quality control (i.e., BioChem quality flags of 1 and 0) are included in the datasets provided here (see Devine et al., 2014 for details on quality control (QC) procedures). In addition to hydrographic and nutrient parameters, datasets further include information on designated regions (e.g., WSS: Western Scotian Shelf, CSS: Central Scotian Shelf, ESS: Eastern Scotian Shelf) as defined in Lehmann et al (2023).

Description: Subsurface nutrients on the Scotian Shelf, an ocean region at the convergence of the subpolar and subtropical western boundary currents (i.e., Labrador Current and Gulf Stream), are chiefly modulated by upstream shelf and slope waters. Yet little is known about long-term fluctuations in the advective transport of nutrients to the shelf. To examine the relationships between subsurface nutrient concentrations and dominant slope water masses at the Scotian Shelf break, we assembled all available hydrographic data (temperature, salinity) and dissolved nutrient data (nitrate, phosphate, silicate) for the period 1975-2020. Hydrographic and nutrient data were extracted from the Fisheries and Oceans Canada (DFO) data archives MEDS (Marine Environmental Data Section Archive; DFO, 2023a) and BioChem (DFO, 2023b; Devine et al., 2014), respectively, and predominantly include data from current DFO programs (e.g., Atlantic Zone Monitoring Program (AZMP)) and legacy datasets. Hydrographic data consist of vertical water column profiles collected using a conductivity-temperature-depth (CTD) profiler, generally mounted to a rosette sampler equipped with Niskin bottles for discrete water and nutrient sampling (Mitchel et al., 2002). Nutrient (nitrate, phosphate, silicate) measurements generally followed well established colorimetric techniques outlined in detail in the AZMP sampling protocol (Mitchell et al., 2002). Only nutrient data that passed initial quality control (i.e., BioChem quality flags of 1 and 0) are included in the datasets provided here (see Devine et al., 2014 for details on quality control (QC) procedures). In addition to hydrographic and nutrient parameters, datasets further include information on designated regions (e.g., WSS: Western Scotian Shelf, CSS: Central Scotian Shelf, ESS: Eastern Scotian Shelf) as defined in Lehmann et al (2023).

Description: Conservation and environmental management are principal countermeasures to the degradation of marine ecosystems and their services.However, in many cases, current practices are insufficient to reverse ecosystem declines. We suggest that restoration ecology, the science underlying the concepts and tools needed to restore ecosystems, must be recognized as an integral element for marine conservation and environmental management. Marine restoration ecology is a young scientific discipline, often with gaps between its application and the supporting science. Bridging these gaps is essential to using restoration as an effective management tool and reversing the decline of marine ecosystems and their services. Ecological restoration should address objectives that include improved ecosystem services, and it therefore should encompass social–ecological elements rather than focusing solely on ecological parameters. We recommend using existing management frameworks to identify clear restoration targets, to apply quantitative tools for assessment, and to make the re-establishment of ecosystem services a criterion for success.

Description: Coral reefs of the world face rapid degradation of biodiversity and ecosystem services, and marine protected areas (MPAs) are a commonly applied solution. Nevertheless, coral reefs continue to decline worldwide, raising questions about the adequacy of management and protection efforts. We argue that expanding the range of MPA targets to also include degraded reefs (i.e. 'DR-MPA'), could help reverse this trend. This approach requires new ecological criteria for MPA design, siting, and management. Rather than focusing solely on preserving healthy reefs, the proposed approach focuses on the potential for biodiversity recovery and renewal of ecosystem services. The new criteria highlight sites with the highest potential for recovery, the greatest resistance to future threats (e.g., temperature and acidification) and the largest contribution to connectivity of MPA networks. The DR-MPA approach is not a substitute for traditional MPA selection criteria; it is rather a complimentary framework when traditional approaches are inadequate. We believe that the DR-MPA approach can help to: 1. Enhance the natural, or restoration-assisted, recovery of degraded reefs and their ecosystem services, 2. Increase the total reef area available for protection, 3. Promote more resilient and better-connected MPA networks, and 4. More effectively contribute to improved conditions for human communities dependent on these ecosystem services. This article is protected by copyright. All rights reserved.

Description: © The Author(s), 2018]. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Global Ecology and Biogeography 27 (2018): 760-786, doi:10.1111/geb.12729.

Description: The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community‐led open‐source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene. The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record. BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km2 (158 cm2) to 100 km2 (1,000,000,000,000 cm2). BioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year. BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates.

Description: European Research Council and EU, Grant/Award Number: AdG‐250189, PoC‐727440 and ERC‐SyG‐2013‐610028; Natural Environmental Research Council, Grant/Award Number: NE/L002531/1; National Science Foundation, Grant/Award Number: DEB‐1237733, DEB‐1456729, 9714103, 0632263, 0856516, 1432277, DEB‐9705814, BSR‐8811902, DEB 9411973, DEB 0080538, DEB 0218039, DEB 0620910, DEB 0963447, DEB‐1546686, DEB‐129764, OCE 95‐21184, OCE‐ 0099226, OCE 03‐52343, OCE‐0623874, OCE‐1031061, OCE‐1336206 and DEB‐1354563; National Science Foundation (LTER) , Grant/Award Number: DEB‐1235828, DEB‐1440297, DBI‐0620409, DEB‐9910514, DEB‐1237517, OCE‐0417412, OCE‐1026851, OCE‐1236905, OCE‐1637396, DEB 1440409, DEB‐0832652, DEB‐0936498, DEB‐0620652, DEB‐1234162 and DEB‐0823293; Fundação para a Ciência e Tecnologia, Grant/Award Number: POPH/FSE SFRH/BD/90469/2012, SFRH/BD/84030/2012, PTDC/BIA‐BIC/111184/2009; SFRH/BD/80488/2011 and PD/BD/52597/2014; Ciência sem Fronteiras/CAPES, Grant/Award Number: 1091/13‐1; Instituto Milenio de Oceanografía, Grant/Award Number: IC120019; ARC Centre of Excellence, Grant/Award Number: CE0561432; NSERC Canada; CONICYT/FONDECYT, Grant/Award Number: 1160026, ICM PO5‐002, CONICYT/FONDECYT, 11110351, 1151094, 1070808 and 1130511; RSF, Grant/Award Number: 14‐50‐00029; Gordon and Betty Moore Foundation, Grant/Award Number: GBMF4563; Catalan Government; Marie Curie Individual Fellowship, Grant/Award Number: QLK5‐CT2002‐51518 and MERG‐CT‐2004‐022065; CNPq, Grant/Award Number: 306170/2015‐9, 475434/2010‐2, 403809/2012‐6 and 561897/2010; FAPESP (São Paulo Research Foundation), Grant/Award Number: 2015/10714‐6, 2015/06743‐0, 2008/10049‐9, 2013/50714‐0 and 1999/09635‐0 e 2013/50718‐5; EU CLIMOOR, Grant/Award Number: ENV4‐CT97‐0694; VULCAN, Grant/Award Number: EVK2‐CT‐2000‐00094; Spanish, Grant/Award Number: REN2000‐0278/CCI, REN2001‐003/GLO and CGL2016‐79835‐P; Catalan, Grant/Award Number: AGAUR SGR‐2014‐453 and SGR‐2017‐1005; DFG, Grant/Award Number: 120/10‐2; Polar Continental Shelf Program; CENPES – PETROBRAS; FAPERJ, Grant/Award Number: E‐26/110.114/2013; German Academic Exchange Service; sDiv; iDiv; New Zealand Department of Conservation; Wellcome Trust, Grant/Award Number: 105621/Z/14/Z; Smithsonian Atherton Seidell Fund; Botanic Gardens and Parks Authority; Research Council of Norway; Conselleria de Innovació, Hisenda i Economia; Yukon Government Herschel Island‐Qikiqtaruk Territorial Park; UK Natural Environment Research Council ShrubTundra Grant, Grant/Award Number: NE/M016323/1; IPY; Memorial University; ArcticNet. DOI: 10.13039/50110000027. Netherlands Organization for Scientific Research in the Tropics NWO, grant W84‐194. Ciências sem Fronteiras and Coordenação de Pessoal de Nível Superior (CAPES, Brazil), Grant/Award Number: 1091/13‐1. National Science foundation (LTER), Award Number: OCE‐9982105, OCE‐0620276, OCE‐1232779. FCT ‐ SFRH / BPD / 82259 / 2011. U.S. Fish and Wildlife Service/State Wildlife federal grant number T‐15. Australian Research Council Centre of Excellence for Coral Reef Studies (CE140100020). Australian Research Council Future Fellowship FT110100609. M.B., A.J., K.P., J.S. received financial support from internal funds of University of Lódź. NSF DEB 1353139. Catalan Government fellowships (DURSI): 1998FI‐00596, 2001BEAI200208, MECD Post‐doctoral fellowship EX2002‐0022. National Science Foundation Award OPP‐1440435. FONDECYT 1141037 and FONDAP 15150003 (IDEAL). CNPq Grant 306595‐2014‐1

Description: © The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Cavanaugh, K. C., Bell, T., Costa, M., Eddy, N. E., Gendall, L., Gleason, M. G., Hessing-Lewis, M., Martone, R., McPherson, M., Pontier, O., Reshitnyk, L., Beas-Luna, R., Carr, M., Caselle, J. E., Cavanaugh, K. C., Miller, R. F., Hamilton, S., Heady, W. N., Hirsh, H. K., Hohman R., Lee L. C., Lorda J., Ray J., Reed D. C., Saccomanno V. R., Schroeder, S. B. A review of the opportunities and challenges for using remote sensing for management of surface-canopy forming kelps. Frontiers in Marine Science, 8, (2021): 753531, https://doi.org/10.3389/fmars.2021.753531.

Description: Surface-canopy forming kelps provide the foundation for ecosystems that are ecologically, culturally, and economically important. However, these kelp forests are naturally dynamic systems that are also threatened by a range of global and local pressures. As a result, there is a need for tools that enable managers to reliably track changes in their distribution, abundance, and health in a timely manner. Remote sensing data availability has increased dramatically in recent years and this data represents a valuable tool for monitoring surface-canopy forming kelps. However, the choice of remote sensing data and analytic approach must be properly matched to management objectives and tailored to the physical and biological characteristics of the region of interest. This review identifies remote sensing datasets and analyses best suited to address different management needs and environmental settings using case studies from the west coast of North America. We highlight the importance of integrating different datasets and approaches to facilitate comparisons across regions and promote coordination of management strategies.

Description: Funding was provided by the Nature Conservancy (Grant No. 02042019-5719), the U.S. National Science Foundation (Grant No. OCE 1831937), and the U.S. Department of Energy ARPA-E (Grant No. DE-AR0000922).

Description: Kelp forests (Order Laminariales) form key biogenic habitats in coastal regions of temperate and Arctic seas worldwide, providing ecosystem services valued in the range of billions of dollars annually. Although local evidence suggests that kelp forests are increasingly threatened by a variety of stressors, no comprehensive global analysis of change in kelp abundances currently exists. Here, we build and analyze a global database of kelp time series spanning the past half-century to assess regional and global trends in kelp abundances. We detected a high degree of geographic variation in trends, with regional variability in the direction and magnitude of change far exceeding a small global average decline (instantaneous rate of change = −0.018 y−1). Our analysis identified declines in 38% of ecoregions for which there are data (−0.015 to −0.18 y−1), increases in 27% of ecoregions (0.015 to 0.11 y−1), and no detectable change in 35% of ecoregions. These spatially variable trajectories reflected regional differences in the drivers of change, uncertainty in some regions owing to poor spatial and temporal data coverage, and the dynamic nature of kelp populations. We conclude that although global drivers could be affecting kelp forests at multiple scales, local stressors and regional variation in the effects of these drivers dominate kelp dynamics, in contrast to many other marine and terrestrial foundation species.